MHD Hybrid Nanofluid Flow Due to Rotating Disk with Heat Absorption and Thermal Slip Effects: An Application of Intelligent Computing

Muhammad Shoaib,Kottakkaran Sooppy Nisar,Muhammad Touseef Sabir,I S Yahia,Bassem F Felemban,Wasim Jamshed,Muhammad Asif Zahoor Raja

doi:10.3390/coatings11121554

Abstract

The objective of this study is to explore the flow features and heat transfer properties of an MHD hybrid nanofluid between two parallel plates under the effects of joule heating and heat absorption/generation (MHD-HFRHT) by utilizing the computational strength of Levenberg–Marquardt Supervised Neural Networks (LM-SNNs). Similarity equations are utilized to reduce the governing PDEs into non-linear ODEs. A reference solution in the form of data sets for MHD-HFRHT flow is obtained by creating different scenarios by varying involved governing parameters such as the Hartman number, rotation parameter, Reynolds number, velocity slip parameter, thermal slip parameter and Prandtl number. These reference data sets for all scenarios are placed for training, validation and testing through LM-SNNs and the obtained results are then compared with reference output to validate the accuracy of the proposed solution methodology. AI-based computational strength with the applicability of LM-SNNs provides an accurate and reliable source for the analysis of the presented fluid-flow system, which has been tested and incorporated for the first time. The stability, performance and convergence of the proposed solution methodology are validated through the numerical and graphical results presented, based on mean square error, error histogram, regression plots and an error-correlation measurement. MSE values of up to the accuracy level of 1 × 10−11 established the worth and reliability of the computational technique. Due to an increase in the Hartmann number, a resistance was observed, resulting in a reduction in the velocity profile. This occurs as the Hartmann number measures the relative implication of drag force that derives from magnetic induction of the velocity of the fluid flow system. However, the Reynolds number accelerates in the velocity profile due to the dominating impact of inertial force.

Highlights

Nowadays, the rapid developments in the field of science and technology demands for more compact and smart devices in terms of better performance, long life and precise operation
The aim of this study is to explore the characteristics of flow as well as heat-transfer abilities in an MHD hybrid nanofluid flow due to rotating disk with heat generation/absorption, velocity and thermal slip effects (MHD-HFRHT) by exploiting the Levenberg–Marquardtbased supervised neural networks (LM-SNNs)
A nonlinear autoregressive (NAR) network, based on sigmoid function [63], is an effective approach based on SNNs for the prediction and estimation of unknown values for a time series by using the re-feeding mechanism for a defined data set, in which the estimated value may again be used as an input for further prediction of new values

Summary

Introduction

The rapid developments in the field of science and technology demands for more compact and smart devices in terms of better performance, long life and precise operation. For this purpose, many efforts have been made in recent decades to improve the rate of heat transfer of various fluids to achieve better thermal and physical properties. Lee et al [3] experimentally calculated the rate of heat transfer for nanofluids consisting of Cu and Al2 O3 nanoparticles Even though, these nanofluidic system fulfill many of the industrial and engineering requirements, researchers are still in search of more efficient nanofluidic systems

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